Course: Mixed Models for Metric Data (3402-451)

Persons:

• Prof. Dr. Hans-Peter Piepho (verantwortlich)

Type of Course:

lecture

In-Class Hours Per Week:

2

Contents:

Multi-stage samples, split plot and split block experiments, degrees of freedom approximations for mixed models (Satterthwaite, Kenward-Roger), longitudinal data, series of experiments, the linear mixed model, Best Linear Unbiased Estimation (BLUE), Best Linear Unbiased Prediction (BLUP), Mixed Model Equations (MME), methods for variance component estimation (REML, ML, MIVQUE0), SAS procedure MIXED, geostatistical methods. Applications are mainly from plant sciences.

Literature:

Kempton R. A., Fox P. N. 1997. Statistical methods for plant variety evaluation. Chapman and Hall, London.

McCulloch, C. E., Searle, S. R. 2001. Generalized, linear, and mixed models. Wiley, New York.

Schabenberger O, Pierce FJ 2002 Contemporary statistical models. CRC Press, Boca Raton.

Location:

Hohenheim

Module:

• 3402-450 Advanced Statistical Methods for Metric and Categorical Data (compulsory)

eLearning:

Course in ILIAS